The most powerful open source data science technologies in your browser.

!!

Yves Hilpisch

I. The Market and The Problem II. How We Solve The Problem

III. Market Size and Facts IV. Strategic Opportunities

Mega Trends Mega trends that influence data science

Dynamic communities shape the way knowledge is transmitted

Today’s standard is “open source”, even for key technologies.

More and more data sets are “open and free”.

Complex analytics work flows are coded in the browser.

Individuals and institutions store more and more data in the cloud.

Infrastructure is a standardized commodity, billed by the hour.

Open Source Software Revolution OSS revolutionizes data science both in the front & back end

In the front end, OSS revolutionizeshow data scientists and developers

work on a daily basis.

FRONT END BACK ENDIn the back end, OSS revolutionizes how

analytics workflows and data applicationsare deployed and scaled.

“DigitalOcean is a simple and fast cloud hosting provider built for developers.

Customers can create a cloud server in 55 seconds, and pricing plans start at only $5 per month for 512MB of RAM, 20GB SSD, 1

CPU, and 1TB Transfer.”

The Problem Obstacles to using OSS for data science

Open Source fast changing environment

Deployment complex, lengthy,

costly, risky

Training how to train and re-train people?

Maintenance how to update,

maintain infrastructure?

Vendors & Partners almost no vendors that provide help & support

Tools multitude of useful

standalone tools

Libraries huge amount of

libraries to manage

Diverse End Users computer & data scientists as well as domain experts

Start where and how to

start, who to talk to?

I. The Market and The Problem II. How We Solve The Problem

III. Market Size and Facts IV. Strategic Opportunities

datapark.io Open source data science technologies in your browser

Tools and technologies data scientists know and love.

!1. Generation: Move Data Around — data analytics started

by moving data from one place to another, analyzing it locally and moving results back to the remote data source

2. Generation: Move Code Around — moving tons of data is costly and time consuming; moving small code sets is faster and less costly

3. Generation: Don't Move Anything — the Browser and Web technologies allow to work directly and in real-time on the infrastructure where data and code are stored (replacing e.g. remote ssh access)

Browser-based Data Science datapark capitalizes on new Web technologies and tools

Feature Rich datapark is essentially a data scientist’s wish list

The Platform Bringing the best of Open Source together in the browser

“Absorb what is useful, discard what is not,!and add what is uniquely your own.”!

—Bruce Lee

Natural Evolution From Python for Finance to Open Source for Data Analytics

PYTHON QUANT PLATFORM

TECHNOLOGY

PRIM

ARY

USE

QUANT PLATFORM

PYTHON DATA ANALYTICS

I. The Market and The Problem II. How We Solve The Problem

III. Market Size and Facts IV. Strategic Opportunities

Data Scientists and Engineers There are about 10mn people in technical computing

Source: diverse Web resources; in mn people

Data Analytics Data analytics is a top priority of almost any organisation

Source: http://www.forbes.com

“Companies will spend an average of $7.4M on data-related initiatives over the next twelve months , with enterprises investing $13.8M, and small & medium businesses (SMBs) investing $1.6M. !80% of enterprises and 63% of small & medium businesses (SMBs) already have deployed or are planning to deploy big data projects in the next twelve months. !83% of organizations are prioritizing structured data initiatives as critical or high priority in 2015, and 36% planning to increase their budgets for data-driven initiatives in 2015.”

Open Source Data Science OS languages dominate data science these days

Poll data from August 2014. Source: http://www.kdnuggets.com

fastest growing

Open Source Data Science R, Python and SQL dominate OS data science

Poll data from August 2014; usage in %. Source: http://www.kdnuggets.com

Vendor Criteria in Data Analytics Integration, security, ease of use & scalability important

Source: 2015 Big Data Analytics Survey (Summary Slides)

open in all directions

decades of Linux

only standards

Docker, Cloud

Platform Competitors … … trying to solve the platform problem for data scientists

Proprietary Notebook solution, closed platform. sense.io

SQL focus, closed platform. modeanalytics.com

Python focus, cloud version not maintained. wakari.io

Major Competitor The major competitor is Jupyter deployed in the cloud

DigitalOcean droplet for 5 USD p.m., Jupyter with Python 3.4, deployed via Docker for 20+ users The MVP: http://jupyter.quant-platform.com

How to maintain, how to ensure security, how to share,

how to control?

I. The Market and The Problem II. How We Solve The Problem

III. Market Size and Facts IV. Strategic Opportunities

Use Cases for datapark.io From teaching to data science to a social app store

Teaching Programming & Data Science

Data Science Platform in Institutions and Corporations

Analytics-as-a-Service for Open Source Projects and

Proprietary Data and Code

Market Place for Ideas, Projects, Apps etc. (“Social Data Science”)

Establishing a Standard Building critical mass & social components, improve scalability

Building out Social Components

Goal of 100,000 users to learn from

Improving Deployment and Scalability

Becoming the Github for Data Science

How do we want to reach our goals Making usage as simple as possible based on standards

Sign-up Two fields only — 30 seconds, immediate full fledged functionality

Open Using standards only — IPYNBs, Linux FS, Dropbox, Drive (easy in/out)

Infrastructure Well established components — Ubuntu, Anaconda, Docker, …

Tools All that you know & love — IPython Notebook, ACE, Shell (Git, Vim), …

Just try it. http://datapark.io

Give us feedback. team@datapark.io

!Dr. Yves J. Hilpisch

!datapark.io | team@datapark.io | @dataparkio

!The Python Quants GmbH